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首页> 外文期刊>Journal of Imaging Science and Technology >Segmentation of Brain Immunohistochemistry Images Using Clustering of Linear Centroids and Regional Shapes
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Segmentation of Brain Immunohistochemistry Images Using Clustering of Linear Centroids and Regional Shapes

机译：使用线性质心和区域形状的聚类分割脑部免疫组织化学图像

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A generalized clustering algorithm utilizing the geometrical shapes of clusters for segmentation of colored brain immunohis-tological images is presented. To simplify the computation, the dimension of vectors composed from the pixel RGB components is reduced from three to two by applying a de-correlation mapping with the orthogonal bases of the eigenvectors of the auto-covariance matrix. Since the brain immunohistochemical images have stretched clusters that appear long and narrow in geometrical shape, we use centroids of straight lines instead of single points to approximate the clusters. An iterative, algorithm is developed to optimize the linear centroids by minimizing the approximation mean-squared error. The partitioning of the two-dimensional vector domain into three portions classifies each image pixel into one of the three classes: The micro-glial cell cytoplasm, the combined hematoxylin stained cell nuclei and the neuropil, and the pale background. Regions of the combined hematoxylin stained cell nuclei and the neuropil are to be separated based on the differences in their regional shapes. The segmentation results of real immunohistochemical images of brain microglia are provided and discussed.
机译：提出了一种通用的聚类算法，利用聚类的几何形状对彩色的脑免疫组织学图像进行分割。为了简化计算，通过对自协方差矩阵的特征向量的正交底进行去相关映射，将由像素RGB分量组成的向量的维数从三个减小为两个。由于大脑的免疫组织化学图像具有呈几何形状长而窄的延伸簇，因此我们使用直线的质心代替单个点来近似簇。通过最小化近似均方误差，开发了一种迭代算法来优化线性质心。将二维矢量域划分为三个部分，将每个图像像素分为三类之一：微胶质细胞胞质，苏木精染色的细胞核和神经纤维质结合体以及浅色背景。苏木精结合的细胞核和神经纤维的区域将根据其区域形状的差异进行分离。提供和讨论了脑小胶质细胞真实免疫组织化学图像的分割结果。

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